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| Mining explicit rules for software process evaluation | |
| Sun, Chengnian (1); Du, Jing (2); Chen, Ning (1); Khoo, Siau-Cheng (1); Yang, Ye (2) | |
| 2013 | |
| 会议名称 | 2013 International Conference on Software and Systems Process, ICSSP 2013 |
| 页码 | 118-125 |
| 会议日期 | May 18, 2013 - May 19, 2013 |
| 会议地点 | San Francisco, CA, United states |
| 收录类别 | EI |
| 出版地 | Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States |
| ISBN | 9781450320627 |
| 部门归属 | (1) School of Computing, National University of Singapore, Singapore; (2) Lab for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, China |
| 摘要 | We present an approach to automatically discovering explicit rules for software process evaluation from evaluation histories. Each rule is a conjunction of a subset of attributes in a process execution, characterizing why the execution is normal or anomalous. The discovered rules can be used for stakeholder as expertise to avoid mistakes in the future, thus improving software process quality; it can also be used to compose a classifier to automatically evaluate future process execution. We formulate this problem as a contrasting itemset mining task, and employ the branch-and-bound technique to speed up mining by pruning search space. We have applied the proposed approach to four real industrial projects in a commercial bank. Our empirical studies show that the discovered rules can precisely pinpoint the cause of all anomalous executions, and the classifier built on the rules is able to accurately classify unknown process executions into the normal or anomalous class. Copyright 2013 ACM.; We present an approach to automatically discovering explicit rules for software process evaluation from evaluation histories. Each rule is a conjunction of a subset of attributes in a process execution, characterizing why the execution is normal or anomalous. The discovered rules can be used for stakeholder as expertise to avoid mistakes in the future, thus improving software process quality; it can also be used to compose a classifier to automatically evaluate future process execution. We formulate this problem as a contrasting itemset mining task, and employ the branch-and-bound technique to speed up mining by pruning search space. We have applied the proposed approach to four real industrial projects in a commercial bank. Our empirical studies show that the discovered rules can precisely pinpoint the cause of all anomalous executions, and the classifier built on the rules is able to accurately classify unknown process executions into the normal or anomalous class. Copyright 2013 ACM. |
| 语种 | 英语 |
| 内容类型 | 会议论文 |
| URI标识 | http://ir.iscas.ac.cn/handle/311060/16645 |
| 专题 | 中国科学院软件研究所 |
| 推荐引用方式 GB/T 7714 | Sun, Chengnian ,Du, Jing ,Chen, Ning ,et al. Mining explicit rules for software process evaluation[C]. Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States,2013:118-125. |
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